volume | PIER Journals

Abstract

Breast cancer is, by far, the most diagnosed disease for the death of women worldwide. Researchers are working with an alternative technology to detect the tumours before it reaches the terrible stage because of the numerous limitations in the current imaging approach. This article suggests a promising technique by utilising non-ionizing microwave signal and artificial intelligence especially deep learning algorithms for early detection of breast cancer. This contribution will present a method to detect and classify the tumour category using backscatter signals obtained from antenna simulation in CST microwave studio software. The post-processed scattering parameters are utilized to create image through MATLAB programming environment. The high intensity in the image represents the precise position of tumour. The automatic classification of tumour is achieved by YOLOv5 deep learning model from the created microwave images. A training dataset with fifty image samples are formed by preprocessing and then augmentation is applied to create final dataset with 1000 samples. This approach can identify the location and type of early-stage tumour with size of 5 mm.

1. Brenner, D. R., H. K. Weir, A. A. Demers, L. F. Ellison, C. Louzado, A. Shaw, D. Turner, R. R. Woods, and L. M. Smith, "Projected estimates of cancer in Canada in 2020," Canadian Medical Association Journal, Vol. 192, No. 9, E199-E205, 2020.
doi:10.1503/cmaj.191292 Google Scholar

2. Canadian Cancer Statistics Advisory Committee "Canadian cancer statistics 2019,", Toronto, ON, Canadian Cancer Society, 2019, Accessed: 2020-04-09. Google Scholar

3. Gøtzsche, P. C. and K. J. Jørgensen, "Screening for breast cancer with mammography," Cochrane Database of Systematic Reviews, Vol. 22, No. 1469-493X (Electronic), CD001877, 2013. Google Scholar

4. Bourqui, J., E. C. Fear, and S. Member, "System for bulk dielectric permittivity estimation of breast tissues at microwave frequencies," IEEE Transactions on Microwave Theory and Techniques, Vol. 64, No. 9, 3001-3009, 2016.
doi:10.1109/TMTT.2016.2586486 Google Scholar

5. Kelly, K. M., J. Dean, W. S. Comulada, and S. J. Lee, "Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts," European Radiology, Vol. 20, No. 3, 734-742, 2010.
doi:10.1007/s00330-009-1588-y Google Scholar

6. Lalitha, K. and J. Manjula, "Design of UWB antipodal vivaldi antenna with parasitic patch for microwave head imaging system," 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS), 1-6, 2022, doi: 10.1109/IC3SIS54991.2022.9885397. Google Scholar

7. Fouda, A. and F. L. Teixeira, "Ultra-wideband microwave imaging of breast cancer tumours via Bayesian inverse scattering," Journal of Applied Physics, Vol. 115, No. 6, 1-8, 2014.
doi:10.1063/1.4865327 Google Scholar

8. Srinivas, K., G. R. Rao, and A. Govardhan, "Rough-fuzzy classifier: A system to predict the heart disease by blending two different set theories," Arab J. Sci. Eng., Vol. 39, 2857-2868, 2014, https://doi.org/10.1007/s13369-013-0934-1.
doi:10.1007/s13369-013-0934-1 Google Scholar

9. Edwards, K., J. LoVetri, C. Gilmore, and I. Jeffrey, "Machine-learning-enabled recovery of prior information from experimental breast microwave imaging data," Progress In Electromagnetics Research, Vol. 175, 1-11, 2022, doi:10.2528/PIER22051601.
doi:10.2528/PIER22051601 Google Scholar

10. Lai, J. C. Y., C. B. Soh, E. Gunawan, and K. S. Low, "Homogeneous and heterogeneous breast phantoms for ultra-wideband microwave imaging applications," Progress In Electromagnetics Research, Vol. 100, 397-415, 2010.
doi:10.2528/PIER09121103 Google Scholar

11. Selvaraj, V., J. B. J. J. Sheela, R. Krishnan, L. Kandasamy, and S. Devarajulu, "Detection of depth of the tumor in microwave imaging using ground penetrating radar algorithm," Progress In Electromagnetics Research M, Vol. 96, 191-202, 2020, doi:10.2528/PIERM20062201.
doi:10.2528/PIERM20062201 Google Scholar

12. Lalitha, K. and J. Manjula, "Novel method of characterization of dispersive properties of heterogeneous head tissue using microwave sensing and machine learning algorithms," Advanced Electromagnetics, Vol. 11, No. 3, 84-92, Oct. 2022, doi:10.7716/aem. v11i3.1821.
doi:10.7716/aem.v11i3.1821 Google Scholar

13. Khoshdel, V., M. Asefi, A. Ashraf, and J. LoVetri, "Full 3D microwave breast imaging using a deep-learning technique," J. Imag., Vol. 6, No. 8, 80, Aug. 2020.
doi:10.3390/jimaging6080080 Google Scholar

14. Redmon, J., S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 779-788, Jun. 2016. Google Scholar

Dr. Kandasamy Lalitha

Dr. Shreya Reddy K